Abstract: Big data analysis applications often use sparse graph traversal algorithm which characterized by irregular data intensive memory access. For improving performance of memory access in sparse graph traversal algorithm, helper thread prefetching could convert discontinuous locality into continuous-instant spatial-temporal locality effectively by using the shared last level cache of chip multi-processor platforms. Betweenness centrality algorithm was used as a case study, the multi-parameter prefetching model of helper thread and optimized instances were presented and evaluated on commercial CMP platforms Q6600 and I7, the average speedup of betweenness centrality algorithm at different input scale is 1.20 and 1.11 respectively. The experiment results show that helper thread prefetching can improve the performance of irregular applications effectively.